


'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			int							= UNetaDModel(
											    block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")       ,   up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")       ,   )
											return model






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.dummy_uncond_unet
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= ScoreSdeVeScheduler()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= ScoreSdeVePipeline(unet=__snake_case       ,   scheduler=__snake_case    )
											sde_ve.to(__snake_case    )
											sde_ve.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= sde_ve(num_inference_steps=2       ,   output_type="""numpy"""       ,   generator=__snake_case    ).images

											_SCREAMING_SNAKE_CASE					:			Dict							= torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Any							= sde_ve(num_inference_steps=2       ,   output_type="""numpy"""       ,   generator=__snake_case       ,   return_dict=__snake_case    )[
											    0
											]

											_SCREAMING_SNAKE_CASE					:			Tuple							= image[0, -3:, -3:, -1]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2





@slow
@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= """google/ncsnpp-church-256"""
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			int							= ScoreSdeVeScheduler.from_pretrained(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= ScoreSdeVePipeline(unet=__snake_case       ,   scheduler=__snake_case    )
											sde_ve.to(__snake_case    )
											sde_ve.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= sde_ve(num_inference_steps=10       ,   output_type="""numpy"""       ,   generator=__snake_case    ).images

											_SCREAMING_SNAKE_CASE					:			Any							= image[0, -3:, -3:, -1]

											assert image.shape == (1, 256, 256, 3)

											_SCREAMING_SNAKE_CASE					:			int							= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

											assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2








'''simple docstring'''


import math







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

				return exponent == int(SCREAMING_SNAKE_CASE__	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__ = 1 / 1_2345	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= 0
				_SCREAMING_SNAKE_CASE					:			str							= 0

				_SCREAMING_SNAKE_CASE					:			int							= 3
				while True:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= (integer**2 - 1) / 4
								# if candidate is an integer, then there is a partition for k
								if partition_candidate == int(SCREAMING_SNAKE_CASE__	):
												_SCREAMING_SNAKE_CASE					:			int							= int(SCREAMING_SNAKE_CASE__	)
												total_partitions += 1
												if check_partition_perfect(SCREAMING_SNAKE_CASE__	):
																perfect_partitions += 1
								if perfect_partitions > 0:
												if perfect_partitions / total_partitions < max_proportion:
																return int(SCREAMING_SNAKE_CASE__	)
								integer += 1


if __name__ == "__main__":
				print(F"{solution() = }")




